Can ESG Ratings Stimulate Corporate Green Innovation? Evidence from China

Abstract

Green innovation serves as both a catalyst for businesses to pursue sustainable development and a crucial step in achieving green circular economic development. Green innovation is the practice of organizations considering environmental, social, and governance (ESG) aspects and the ESG advantages resulting from this process may become a driving force for enterprises to undergo a green transformation. Therefore, based on data related to Chinese A-share listed companies from 2009 to 2020, we study the relationship between ESG rating performance and corporate green innovation and its boundary mechanism. The results show that ESG ratings can improve the green innovation level of listed enterprises, and the relationship between ESG ratings and green innovation was also found to be strengthened by the institutional environment and redundant organizational resources. This study previously confirmed the positive impact of enterprises’ ESG ratings on their green innovation, which has important implications for realizing the effective combination of ESG advantages and green innovation, promoting the construction of an ecological civilization, and realizing the concept of a community with a shared future for mankind.

1. Introduction

Enterprises are reforming their business philosophies in response to the necessity for social responsibility brought on by sustainable development. Friedman once held that enterprises’ main social obligation is to maximize their profits [1]. Other accountability obligations imposed on enterprises are infeasible and disrupt the market economy. However, the increasingly prominent social issues related to sustainability, such as climate change, wealth disparity, and infectious diseases, as well as frequent political resistance to national response actions [2], have led to differing views on maximizing shareholders’ interests [3]. Enterprises are required to incorporate environmental (E), social (S), and governance (G) factors into investment decisions and seek solutions to social problems at the enterprise level by practicing ESG concepts combined with the coordination of international organizations. Investors and government policies are presently highly interested in ESG. The impact of COVID-19 is increasing the market share of global responsible investment in response to the green recovery. The number of institutions endorsing the United Nations-supported principles of responsible investment surged by 28% in 2020 [3]. Despite the late start of China’s ESG growth, all facets of society have given it more attention. ESG policies and investments have been greatly improved, particularly since Chinese Chairman Xi proposed the UN General Assembly’s “2060 carbon neutrality” objective.

A key indicator of corporate sustainable development and environmental, social, and corporate governance (ESG) is the expansion and enrichment of the socially responsible investing (SRI) concept [4,5]. A growing number of businesses now accept the ratings provided by ESG rating firms as the green concept gains traction. This indicates that academic attention is now heavily focused on how ESG ratings affect company strategy [6]. Existing research on the effectiveness of ESG ratings is unfortunately frequently disputed. According to academics who support ESG ratings, such evaluations objectively and effectively gauge a company’s ESG efforts through its competitive advantage, social reputation, and operating performance; give stakeholders access to resources [7]; reduce regulatory and reputational risks; and provide stakeholders with comprehensive and comparable data to correct information asymmetries [8]. A company’s better ESG performance has lower equity capital costs [9] and loan costs [10]. ESG should be a key instrument for improving corporate finance in a negative loan market [11]. Moreover, ESG can help firms to hedge risks [12], build trust against shocks in a crisis [13], and improve corporate performance and long-term value [14]. In contrast, other scholars believe that ESG ratings are ineffective, arguing that they lead to symbolic compliance with external requirements in order to obtain various benefits, which may not be effective in improving corporate sustainability behavior [15]; rather, they represent institutional regression and may mislead stakeholders [16,17]. These contrasting views have led to a large number of studies on the effectiveness of ESG ratings, most of which have examined the relationship between ESG ratings and firm financial performance [18,19] or capital market reactions [20,21]. These studies have undoubtedly contributed significantly to our understanding of the impact of ESG; however, few studies have examined corporate sustainability investment responses following ESG ratings, and even fewer have focused on their impact on corporate green innovation. Meanwhile, “Dual externalities” based on green innovation highlight green technology [22]. At the macro level, green innovation technology’s external spillover effect lowers costs, improves environmental protection, and achieves high-quality economic development; at the micro level, businesses reshape their development models, introduce new ideas for reducing emissions and preserving energy, and encourage the green transformation of enterprises. As a result, we are as yet unable to foresee how ESG would affect corporate strategic decision making about green innovation and the aforementioned relationship’s mechanisms [23]. In view of the actions of organizations and the significance of their participation in sustainable development activities, this research examines the impact of ESG ratings on the green innovation strategies of businesses to make up for deficiencies in pertinent domains [24].

As a result, the theoretical impact and boundary mechanism of ESG ratings on companies′ green innovation are considered to be the primary focus of this article. The non-financial companies listed on the A-share market in China from 2009 to 2020 are the research subjects of this paper, which also describes the complete green innovation activities of the companies in conjunction with data from the World Intellectual Property Organization and authoritative green innovation patents. It also measures the performance level of ESG using data from ESG ratings and emphasizes the influence of ESG ratings on the level of green innovation of the companies. This research also illustrates a potential border mechanism from the redundant organizational resources and institutional environment perspectives. The following conclusions are obtained: (i) ESG ratings have significantly promoted green innovation; (ii) the completeness of the institutional environment and the abundance of redundant organizational resources have strengthened the positive impact of ESG ratings on corporate green innovation.

The remainder of the paper is organized as follows: Section 2 proposes the relevant theories and hypotheses; Section 3 describes the data and methodology; Section 4 provides empirical findings; Section 5 provides the study’s conclusions.

2. Literature Review and Hypothesis Development

2.1. Literature Review

This section presents a list of empirical studies that use GI variables and ESG consequence variables in their models (Table 1). The list provides the variables most often associated with GI and ESG and suggests where to find theoretical or empirical gaps in this research. Nevertheless, a large proportion of the most cited articles in the field concern theoretical works aimed at conceptualizing and developing markers. In this research, the empirical use of GI and ESG variables was carried out in response to the literature studies. This may be because GI and ESG research is still in its infancy or because there are different and interchangeable terms to refer to the same topic. Table 1 provides the names of the authors, journal titles, and variables used in each study. We assessed and distinguished between the types of variables to identify the roles associated with GI: GI drivers (modeling GI as dependent variables), GI outcomes (modeling GI as independent variables), and moderating or mediating variables. In addition, the variables and associated impact mechanisms of ESG on the strategic consequences of the firms were also assessed.

In studies on corporate green innovation, “consequential performance impacts” and “drivers” were the most common categories. On the one hand, the independent variables as drivers of green innovation in existing studies included corporate social responsibility practices [30], customer pressure [31], knowledge management processes [32], environmental legitimacy [33], government subsidy [34], government’s awareness of environmental protection [35], internal and external financing [36], institutional investors’ site visits [37], green credit policy [38], etc. On the other hand, the main strategic consequences of green innovation included carbon emission performance [25], sustainable development [26], brand value [27], corporate financing [28], and financial performance [29]. The moderating mechanism variables mainly included organizational culture [31], sustainable development practices [32], green absorptive capacity [33], board diversity and independent directors, etc. [34], which were mainly discussed from the perspective of executive characteristics and organizational capacity. Table 1 provides a deeper and more accurate understanding of the variables that were the effective drivers, outcomes, and impact mechanisms of GI. In addition, in terms of ESG consequences, the main focus was on CSR [39], market efficiency [40], financial performance [41], innovation [43], and market value [44], and the moderating mechanisms mainly included board representation, institutional investor ownership, and market sentiment.

In summary, according to Table 1, there is a lack of research on the relationship between ESG and corporate green innovation, whether due to the consequences of the ESG strategy or the influencing factors of green innovation. In particular, in terms of the moderating mechanisms, the internal mechanisms primarily focused on executive characteristics and organizational capacity, with less consideration given to the regulatory mechanisms. The external research focused primarily on market competition and less on the legitimacy of the institutional environment, and research has shown that companies that effectively respond to institutional pressures are more likely to obtain the external resources required for sustainable development by achieving social legitimacy. As a result, from the ESG research perspective, this paper systematically integrated the internal and external moderate mechanism variables of previous studies, combined institutional theory and resource-based perspectives, and introduced two moderate variables, the external institutional environment and internal redundant organizational resources, to examine the boundary conditions of ESG and corporate green innovation, rewarding and extending the existing literature.

2.2. Hypothesis Development

2.2.1. ESG and Corporate Green Innovation

This paper believes that the increasingly stringent social responsibility challenges brought about by the requirements of sustainable development will provide some enterprises that respect environmental, social, and governance factors and gradually accumulate and establish specific ESG performance in practicing the ESG concept with a brand-new competitive advantage in transnational investment. This ESG rating performance deviates from the profit maximization motivation (only applicable to shareholders), which is completed when we promote the enterprise to meet the social responsibility requirements and actively respond to all stakeholders [45]. The term “ESG rating performance” specifically refers to the sum of the valuable and scarce resources, systems, and capabilities established by enterprises in ESG practice, such as green technology, brand, social responsibility system, and other ESG asset advantages and institutional advantages. ESG differs from conventional monopolistic advantages as it emphasizes meeting the demands of shareholders, consumers, suppliers, communities, and other stakeholders and pursues inclusive development, which contributes to economic and social development through interaction with non-market factors. Therefore, we found that the benefits of ESG promoted the green innovation of enterprises by encouraging them to develop new competitive advantages in the following two aspects: First, they surpassed the limitations of traditional asset ownership advantages and created unique asset ownership advantages. ESG practices can assist businesses in modernizing their technology and products, enhancing their capacity for green innovation, and fostering better ties with their employees, suppliers, and other stakeholders [13]. In exceptional circumstances (such as the period of COVID-19), an enterprise will obtain tremendous development if it treats upstream and downstream producers and other stakeholders with sincerity. Meanwhile, ESG practices account for a high proportion of the development of intangible assets [46], and the enhancement of the reputation value for those enterprises that emphasize ESG will actively disclose the information about their social responsibility to the public, both objectively and subjectively, so that the public can judge the social responsibility behavior of the enterprise, and the enterprise can establish a trustworthy reputation [47]. Compared with other wary enterprises, those that have benefited in their asset advantage and reputation due to ESG practices will seize market share in addition to the pursuit of ESG by investors and customers [48]. For example, the financing convenience brought about by the favor of investors and creditors can encourage the green innovation of enterprises as it copes with the stress of capital constraints in green innovation [49]. Therefore, the ESG rating performance can significantly reduce the cost of financing, enhance a company’s overall competitiveness, and help it gain more market share. Schuler and Cording (2006) have proposed that enterprises equipped with a better corporate social performance will be more competitive, and those with a good ESG performance that satisfy the requirements of stakeholders may incur lower financing costs and hold greater market share [50].

Second, they enhance the legitimacy of green innovation investment. The enterprise ESG advantage’s most distinctive feature is its legitimacy. Environmental protection, stakeholders’ rights and the protection of interests, and upholding social obligations are not just what customers and investors expect from corporations but are also slowly becoming what regulatory agencies require of them [51]. Impacted by ESG practices, enterprises regard green innovation investment as an important driving force to promote economic growth and believe that they have made contributions to overall social welfare due to protecting the interests of stakeholders [51]. On the contrary, if an enterprise violates the ESG concept, it may result in the rapid spread of public concern, impairing its enterprise image and brand reputation significantly; hence, the legitimacy of enterprise operations will become more prominent [52].

In addition, the endogenous institutional advantages derived from social responsibility formed around the ESG concept are also conducive to embedding the value system of the business strategy, thus reducing the pressure of institutional isomorphism [52]. ESG advantages address the legal issue of green innovation and endow enterprises with flexible green innovation strategies, which in turn, improve the level of green innovation. In addition, the general recognition of the legitimacy of the ESG concept lowers the transfer costs of the green technology, brand reputation, and institutional mechanism obtained through ESG practices, facilitating the production of green innovation resources and realizing the driving effect of ESG advantages on enterprise strategy [53]. The green innovation method utilizes internal transfer in addition to ESG advantages and can internalize open market transaction expenses to obtain internalized advantages [53]. Enterprises that adopt market mechanisms will run into issues with market failures such as information asymmetry between parties in technology license agreements and horizontal externalities brought on by brand sharing. Green innovation can minimize market transaction costs and take advantage of organizations′ economies of scale and scope by placing essential assets under shared control. Therefore, hypothesis 1 is proposed:

Hypothesis 1.

ESG rating performance has a positive impact on corporate green innovation.

2.2.2. Moderation Effect of Institutional Environment

According to institutional theory, the institutional environment has a crucial influence on the structure and behavior of an organization [54]. An efficient legal system, good government market relations, and well-developed product and factor markets can support healthy market development by facilitating information flow, supporting competition, and minimizing externalities [55]. Firms that respond effectively to institutional pressures and comply with norms and rules have easier access to the external resources needed for survival and growth by ensuring social legitimacy [56]. Accordingly, in order to operate smoothly and stably, firms need to respond to institutional pressures [57] and the level of institutional pressures affects the strategic choices and effectiveness of firms [58]. Meanwhile, the resources and regulations on which green innovation depends are key variables for innovation, whereas the access to core resources depends on a combination of market-based and non-market-based strategies and is determined by the institutional environment [39]. For example, companies need to improve their ESG rating performance to respond to stakeholder pressure [59]. Therefore, the impact of firm ESG ratings on green innovation may vary depending on the role of the pressures from the institutional environment, and it is necessary to investigate the impact of each type of institutional pressure [60], which can be classified as coercive, normative, or imitative, on green innovation by interacting with firm ESG ratings.

First, coercive pressures are defined as “formal or informal pressures arising from the institutions and regulations of the society to which the firm belongs” [61]. Coercive pressures resulting from the influence of those in power, such as regulations by government agencies, can shape the standards of corporate behavior and encourage firms to adopt or reinforce certain behaviors [60]. In this regard, ESG-related government policies and regulations constitute coercive pressures on firms and influence their expectations for green innovation strategies. Second, imitation pressures refer to the pressure to imitate competitors’ behavior, which is identified as success. A firm that adapts to imitation pressures is more likely to protect itself from potential losses and gain legitimacy in its decisions. Thus, it imitates leading competitors to ensure social legitimacy and decision legitimacy [61,62]. From this perspective, the ESG rating performance reflects the imitation of peer organizations’ behavior, which is perceived as successful by stakeholders in a similar organizational structure [62]. For example, manufacturers tend to imitate their competitors in managing extended supply chain activities in order to achieve sustainably managed carbon reduction targets. This measure can increase corporate green innovation by enhancing the legitimacy and legality of corporate activities [63]. Normative pressures come from the values and standards of behavior recommended and expected by external stakeholders [64]. Firms must understand and comply with the standards, norms, and expectations of external stakeholders in order to achieve social legitimacy. In particular, customer demands form a key normative pressure [65], which can be an important driver for companies to enhance certain activities [60]. For example, positive consumer perception of a firm’s environmentally friendly products and socially responsible activities can change a firm’s green innovation decisions by acting as a normative pressure [66].

Thus, on the one hand, in regions with relatively well-developed institutional environments, the three institutional pressures mentioned above enhance the impact of ESG ratings on a firm’s reputation and image, and the key factors of the ESG rating performance depend mainly on its market-oriented operations and management capabilities, which are consistent with entrepreneurs’ knowledge and social legitimacy perception structures. This will undoubtedly strengthen the role of the ESG rating performance in promoting corporate green innovation strategies. On the other hand, a sound institutional environment can provide the necessary property rights protection, guarantee the benefits of strategic decisions, enhance the long-term orientation of decision makers, further strengthen the motivation of enterprises to engage in green innovation driven by ESG performance [67], effectively protect the green innovation achievements of enterprises, and enhance their willingness to adopt green innovation. Therefore, hypothesis 2 is proposed.

Hypothesis 2.

The more perfect the institutional environment, the stronger the positive relationship between the ESG rating performance and corporate green innovation.

2.2.3. Moderation Effect of Redundant Organizational Resources

Resource redundancy in organizations is defined as resources kept within an organization beyond their usefulness and serves as a practicable or potential resource buffer to enhance an organization’s ability to respond to unexpected changes in the external environment and devise strategies [68]. At the organizational level, green innovation strategies emphasize that organizations should make greater resource investments [64], and having more disposable resources facilitates an organization’s ability to operate and that an organization’s redundant resources can provide resource support for green innovation strategies. In particular, the increasing level of redundant resources for disposal coincides with an organization’s ability to implement green innovation behaviors and behavioral freedom [69]. Gruber (2010) showed that a higher degree of redundancy in organizational resources facilitates an organization to try more unpredictable occurrences and supports an organization to search for opportunities and problem solutions on a larger scale [70]. At the managerial level, redundant organizational resources increase the risk perception of managers’ decisions. For decision makers, the degree of risk perception is an important reference point for their strategic choices, and managers’ preferences for strategic decisions depend on the impact of the same decision risk on different organizations [71]. Managers may make riskier strategic choices when organizations are more resilient to external risks as a result of the high level of redundant organizational resources, whereas they tend to be more conservative when making decisions [72]. Consequently, the ESG rating performance may have a greater impact on corporate green innovation in the context of abundant redundant organizational resources. Figure 1 shows the theoretical research model, as follow.

Hypothesis 3.

The higher degree of redundant organizational resources will strengthen the positive relationship between ESG rating performance and corporate green innovation.

3. Data and Methodology

3.1. Data and Samples

This paper investigated the impact of the ESG rating announcement on green innovation for the listed companies of the Shanghai and Shenzhen Stock Exchange from 2009 to 2020 as the research case. The specific case selection process was as follows: (i) exclude listed finance and insurance companies that greatly differed from other companies in terms of the main business, company size, and information disclosure in China; (ii) exclude special treatment (ST)-listed companies that greatly differed from other companies in financial indicators and information disclosure; (iii) exclude the companies listed in that year that had a shorter listing period and historical information duration, and greatly differed from other companies in information disclosure; (iv) eliminate the missing samples such as green innovation patents and control variables; (v) obtain a total of 21,616 observations. This paper employed Winsor2 to perform the tailing treatment on the continuous variables at the levels of 0.01 and 0.99 to mitigate the impact of extreme values on the empirical results. ESG rating data were collected from the Wind database and other data were from the CSMAR database. The green innovation data were derived from the patent classification number data of the China research data service platform (CNRDS) and the “International Patent Classification green list” issued in 2010 by the World Intellectual Property Organization (WIPO).

3.2. Variables Definition

3.2.1. Dependent Variable

Green innovation. The number of patents is an essential indicator of the green innovation level of an enterprise. As reported by Ko et al. (2021) [73], we applied the natural logarithm of the sum of the total number of applications of green innovation patents and utility patents plus 1 to measure corporate green innovation (GRInno). We classified the application of green invention patents as exploratory green innovation (GRInva) and the application of utility patents as exploitative green innovation (GRUma). Furthermore, we employed various green innovation patent applications as the dependent variable (corporate green innovation) for the robustness test to promote the reliability of the research results.

3.2.2. Independent Variable

ESG Rating (ESG). All the listed companies were rated as nine by the China Securities ESG rating, and the ratings from low to high are C, CC, CCC, B, BB, BBB, A, AA, and AAA. The ESG rating of the enterprise was assigned a number from 1 to 9 from low to high in the regression analysis in reference to the practice of Lin et al. (2021)13 [8]. The ESG rating increases as the value increases.

3.2.3. Moderation Variables

Institutional Environment (InEnv). This paper refers to the research of Wei et al. (2011). Currently, the degree of marketization is often used as its proxy variable to measure the regional institutional environment. Therefore, this paper judged the external institutional environment based on existing research data and the marketization index of the province where the enterprise was located [74]. A larger index and a higher index produce a better institutional environment.

Redundant organizational resources (OrRes). The cash ratio was adopted to determine the redundant organizational resources given their importance in firms′ green innovation via referring to the research of Kim and Bettis (2014). A higher ratio is simultaneous with richer redundant organizational resources [75].

3.2.4. Control Variables

We controlled for the following variables at the firm level: firm size (total assets), firm age (years since the firm’s founding), Top1 (largest shareholder), ROA (return on asset to proxy for firm performance), and Lev (asset–liability ratio). To control for other important characteristics of boards and CEOs, we included the following variables in our models: Board (natural logarithm of the number of directors); Inde (independent director), which denoted the proportion of independent directors on the board; and Dualiy (CEO and chairman duality), which indicated the overlap of the CEO and Chairman of the board. In addition, this paper controlled the industry effects (Industry) and year effects (Year) on firms′ green innovation, respectively.

Based on this, the descriptions of the variables are summarized in Table 2.

3.3. Models Establishment

In order to verify the influence of ESG ratings on corporate green innovation and the moderating effect of the institutional environment and redundant organizational resources, the following regression models were established in reference to the model settings of Wen and Zhou, 2017.

(1)${\mathrm{GRInno}}_{\mathrm{i},\mathrm{t}+1}{=\mathsf{\alpha }}_0{+\mathsf{\alpha }}_1{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_{\mathrm{k}}{\displaystyle \sum }{\mathrm{Control}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\mu }}_{\mathrm{i}}{+\mathsf{\delta }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$

(2)${\mathrm{GRInva}}_{\mathrm{i},\mathrm{t}+1}{=\mathsf{\alpha }}_0{+\mathsf{\alpha }}_1{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_{\mathrm{k}}{\displaystyle \sum }{\mathrm{Control}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\mu }}_{\mathrm{i}}{+\mathsf{\delta }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$

(3)${\mathrm{GRUma}}_{\mathrm{i},\mathrm{t}+1}{=\mathsf{\alpha }}_0{+\mathsf{\alpha }}_1{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_{\mathrm{k}}{\displaystyle \sum }{\mathrm{Control}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\mu }}_{\mathrm{i}}{+\mathsf{\delta }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$

(4)${\mathrm{GRInno}}_{\mathrm{i},\mathrm{t}+1}{=\mathsf{\alpha }}_0{+\mathsf{\alpha }}_1{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_2{\mathrm{InEnv}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_3{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{ \times \mathrm{InEnv}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_{\mathrm{k}}{\displaystyle \sum }{\mathrm{Control}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\mu }}_{\mathrm{i}}{+\mathsf{\delta }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$

(5)${\mathrm{GRInno}}_{\mathrm{i},\mathrm{t}+1}{=\mathsf{\alpha }}_0{+\mathsf{\alpha }}_1{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_2{\mathrm{OrRes}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_3{\mathrm{ESG}}_{\mathrm{i},\mathrm{t}}{\times \mathrm{OrRes}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\alpha }}_{\mathrm{k}}{\displaystyle \sum }{\mathrm{Control}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\mu }}_{\mathrm{i}}{+\mathsf{\delta }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$

In the above formulas, subscript i represents the listed company; t represents the year; ε represents the random error term; GRInno represents corporate green innovation; GRInva and GRUma represent exploratory green innovation and exploitative green innovation, respectively; ESG represents the ESG rating; InEnv represents the institutional environment; OrRes represents redundant organizational resources; and Control represents the control variables.

4. Empirical Findings

4.1. Descriptive Statistics and Correlation Analysis

Table 3 provides the descriptive statistics and correlation test results. The GRInno’s mean value of 0.445 and the standard deviation of 0.852 indicate that most companies involved have conducted green innovation activities as required by the research, whereas their green innovation levels had some exceptions. The mean and standard deviation values of ESG were 4.070 and 1.054, respectively, indicating that the overall level of ESG ratings of China’s A-share listed companies was at a medium level, and various companies shared similar ESG ratings. The mean value of the institutional environment was 8.150 and the standard deviation was 1.934, demonstrating that the regional institutional environment of the listed companies was relatively perfect. The mean value of redundant organizational resources was 0.968 and the standard deviation was 1.698, showing that each listed company held unique redundant organizational resources. The correlation coefficient of ESG and corporate green innovation was 0.159 and they had a significant positive correlation at a level of 1%, suggesting that ESG ratings had a promoting effect on corporate green innovation. H1 was primarily verified. Furthermore, the fact that the main variables under investigation had standard deviations that were comparatively high and correlation coefficients that were much lower than 0.8 suggests that there was no multicollinearity between the variables, making them appropriate for multiple regression analysis.

4.2. Multiple Regression Analysis

4.2.1. The Effect of ESG Ratings on Corporate Green Innovation

Table 4 reports the impact of ESG ratings on companies′ green innovation. H1 predicted that the ESG rating would be positively correlated with the companiess’ green innovation. First, model 1 in Table 4 reveals a significant positive correlation between ESG ratings and the green innovation of the companies (coef. = 0.019, p < 0.01). In addition, model 2 and model 3 in Table 4 show that ESG ratings had a significant positive correlation with exploratory green innovation and exploitative green innovation (coef. = 0.017, p < 0.01; coef. = 0.011, p < 0.01). Therefore, hypothesis 1 in this study is supported.

4.2.2. The Moderating Effect of Institutional Environment and Redundant Organizational Resources

Table 5 shows the moderating effects of the institutional environment and redundant organizational resources. H2 predicted that the institutional environment would strengthen the positive relationship between ESG ratings and corporate green innovation. From model 1 in Table 5, it can be seen that the interaction term between ESG ratings and the institutional environment had a significant correlation with corporate green innovation (coef. = 0.009, p < 0.01). The institutional environment strengthened the positive relationship between ESG ratings and corporate green innovation. Therefore, H2 is partially supported.

H3 predicted that redundant organizational resources would strengthen the positive relationship between ESG ratings and green innovation. Model 2 in Table 5 shows that the interaction term between ESG ratings and redundant organizational resources had a significant positive correlation with the share of corporate green innovation (coef. = 0.067, p < 0.05), demonstrating that redundant organizational resources strengthened the positive relationship between ESG ratings and corporate green innovation. Therefore, H3 is supported. In addition, Model 3 in Table 5 demonstrates that the aforementioned two moderating factors were included in the integration model for the regression test, and the results indicate the validity of the conclusions.

4.3. Robustness Test

4.3.1. The Change of the Measurement Method of Corporate Green Innovation

This paper adopted various green innovation variables to test robustness. Specifically, we classified the application of green innovation patents as exploratory green innovation (GRinva) and the application of utility patents as exploitative green innovation (GRuma). Table 6 indicates the robustness of this paper concluded from the regression results similar to those described in previous sections.

4.3.2. The Change of the Measurement Method of Standard Errors

This paper has referred to existing research methods. This paper employed Driscoll–Kraay standard errors to estimate the standard errors to remove the possible heteroscedasticity [76], cross-sectional correlation, and sequence correlation of panel data, with the regression results shown in Table 7. The study found that the research hypotheses were still tenable with a high validity performance upon replacing the regression standard error calculation method.

5. Conclusions and Discussion

5.1. Conclusions

Public awareness of sustainable development worldwide has focused attention on green innovation. This green innovation trend may reshape the competitive advantages of enterprises and have a profound impact on their green transformation and development. Therefore, this paper believes that the specific advantages of pursuing inclusive social development with ESG as the core will become particularly significant in green innovation strategies in comparison with the traditional pursuits of private profits. Based on the panel data of China’s A-share listed companies from 2009 to 2020, this paper employed the fixed effect model to empirically analyze the relationship between the ESG ratings of the sample enterprises and the green innovation performances of the enterprises and further tested the regulatory effects of the external institutional environment and the redundant resources of the internal organization. The following conclusions were made: (i) ESG ratings significantly promoted green innovation; (ii) the completeness of the institutional environment and the abundance of organizational redundancy strengthened the positive impact of ESG ratings on corporate green innovation. The findings demonstrate that businesses can successfully react to a complex business environment thanks to the strategic flexibility provided by their ESG rating performance. The ESG rating performance, however, can encourage green innovation and enterprise growth through positive spillover channels such as the demonstration effect.

China is at a critical stage of building a green circular development and striving to transform it into a sustainable economic model. Given the important value of ESG ratings in the green innovation of enterprises, accelerating the promotion of ESG governance, optimizing the cultivation of enterprises with ESG advantages, and striving to create new advantages in green competition conform to the internal requirements of the concept of sustainable development and offer a vital opportunity to improve the high-quality green development of the economy. These research findings have the following practical implications.

Members of boards of directors should take the initiative by taking responsibility for environmental preservation and social responsibility while concentrating on corporate governance and non-financial performance in order to maintain sustainable development and a competitive advantage. A high ESG performance also means that the enterprise will have an advantage over its rivals in the market. Board members should focus on improving their ESG performance in order to enhance their corporate green innovation capabilities, attract value investment from investors with an ESG bias, and generate higher market competitiveness. This is due to the ESG performance playing a key role in promoting corporate green innovation. Moreover, board members should take the initiative by assuming environmental protection and social responsibilities, placing emphasis on corporate governance and non-financial performance, performing cognitive modeling of the ESG concept and the allocation of redundant organizational resources to green innovation strategies, and forming a green sustainable development system.

For strategic investors, an enterprise‘s ESG performance is critical since it somewhat indicates how well-run and innovative the business is. ESG scores are therefore non-financial indicators that investors can use to evaluate the worth of a company’s investment when choosing investment targets in order to better manage risks and earn matching long-term returns.

As policymakers, the government should focus more attention on the encouragement and promotion of ESG. To further encourage businesses to adopt sound ESG ideas and advance the development of their environmental, social, and corporate governance capabilities, the government and pertinent ministries should release related policies and privileged regulations, which are beneficial for improving the regional institutional environment and promoting the development of corporate green innovation.

From a national standpoint, the introduction of a system of rewards and punishments for corporate ESG performance should be encouraged. Through a variety of incentives, including tax rebates, green credit incentives, and lowered entry barriers, the government can encourage businesses to enhance their ESG performance and advance sustainable socio-economic growth. The government should actively recognize the stimulating impact of government subsidies on green innovation and provide incentives to creative businesses. In order to encourage green innovation, the government should aggressively recognize the role that subsidies play in the process and provide financial support to forward-thinking businesses.

5.2. Discussion

To make ESRs more sustainable, the government could potentially raise taxes and other penalties. Through punitive measures such as tax increases, the government can also force companies with poor ESG performance to improve their performance. The government should also actively promote an ESG disclosure system that is broadly applicable so that businesses, investors, and the general public pay more attention to the ESG performance of businesses and support the transition to a green economy. The main contributions of this study are as follows. First, it responds to the debate surrounding the effectiveness of ESG in emerging markets by providing new insights into the relationship between corporate ESG ratings and green innovation and their mechanisms of action. Existing research on ESG ratings has focused on developed markets that have developed relatively mature concepts. For example, Chouaibi (2021) analyzed the role of ESG practices in the UK and German systems in improving financial performance by benefiting from green innovation [77]. Meanwhile, Cohen (2020) reached a different conclusion and pointed out the opposite relationship between ESG scores and green innovation in the U.S. energy sector [78]. Existing studies are limited and focus on developed countries with relatively mature ESG systems. In contrast, emerging markets are still in their infancy due to significant institutional differences. The findings of this study help to ascertain whether ESG ratings have contributed to the green transition and enriched the results related to ESG practices in developing countries. Second, this study further expands the influencing factors of green innovation beyond the mainstream research paradigm of government-level environmental regulations on green innovation, with special emphasis on third-party rating agencies, providing new perspectives and empirical support for the guiding role of ESG ratings on green innovation. Third, this study expands the scope of green innovation measurement to compensate for the existing literature’s preference for green innovation in terms of quantity rather than quality. Drawing on detailed patent census data in China, this study uses green patent citations to measure the quality of green innovation, which is conducive to deriving more meaningful research questions, emphasizing objective data on green innovation patent applications, and improving their impact. Fourth, this study analyzes the moderating mechanism between ESG ratings and green innovation through the external institutional environment and internal redundant organizational resources, deconstructs the internal logic, extends the research boundary, and provides a realistic reference for the promotion of ESG ratings by examining the heterogeneity in the Chinese context from multiple dimensions such as institutions, markets, and firms as emerging markets continue to develop.

This study provides a new theoretical framework for corporate green innovation by highlighting the importance of the external institutional environment and internal redundant organizational resources for corporate green innovation. However, there are still some limitations in our research.

First, the primary limitation of our study is linked to the nature of our sample, which only included companies in China, and companies listed in other emerging countries should be included. Meanwhile, other factors influencing ESG performance (e.g., systematic risk and board structure) should also be taken into consideration in further research. From a future research perspective, we could examine other factors affecting the market. In the future, we could obtain data from other third-party platforms and involve more samples with different languages. Some alternative variables could measure green innovation more than the patent application measurement, and updated research data could replace the relevant indicators to make a comprehensive measurement of the level of green innovation.

Second, the findings of this paper are also limited in terms of the mechanisms that enhance corporate green innovation, which need to be explored and further tested. In terms of future research directions, in relation to the moderating mechanisms, although this study investigated the institutional environment and redundant organizational resources as the moderating factors, other factors such as political capital (Lin et al., 2015), environmental ethics (El-Kassar and Singh, 2019), or top management characteristics (Galbreath, 2019) could also influence the relationship between the ESG ratings and corporate green innovation. In addition, we plan to conduct a heterogeneity analysis concerning the discrepancies in diverse industries and property rights to reveal the impact level of ESG ratings on corporate green innovation.

Author Contributions

H.L. and C.L. conceived this topic and designed the empirical methodology; Writing—original draft, H.L.; Writing—review and editing, H.L. and C.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. Theoretical research model.

Table 1

Selected prior empirical research on GI and ESG.

Author	Journal Titles	Drivers of GI	Outcomes of GI	Moderating Variables	Mediating Variables
Research literature on the outcomes of green innovation (GI)
Xu et al. (2021) [25]	Energy Economics		carbon emission performance		energy consumption structure effect, industrial structure effect, urbanization effect, and foreign direct investment (FDI) effect
Huang et al. (2021) [26]	Technology in Society		sustainable development		marketization, local government competition
Lin et al. (2021) [27]	Technological Forecasting and Social Change		brand value		marketing capability and R&D intensity
Javeed et al. (2022) [28]	International Journal of Environmental Research and Public Health		Corporate Financing	Corporate Social Responsibility and Gender Diversity
Aguilera-Caracuel and Ortiz-de-Mandojana (2013) [29]	Organization and Environment		Financial performance	national institutional conditions
Research literature on influencing factors of green innovation (GI)
Yuan and Cao (2022) [30]	Technology in Society	corporate social responsibility practices			green dynamic capability
Chu et al. (2018) [31]	The International Journal of Logistics Management	Customer pressure		organizational culture
Shahzad et al. (2021) [32]	Business Strategy and the Environment	knowledge management process		sustainable development practices
Zhou et al. (2021) [33]	Resources Policy	environmental legitimacy		green absorptive capacity	Senior management cognition and green strategic orientation
Xia et al. (2022) [34]	Energy Policy	Government subsidy		board diversity and independent directors
Fang et al. (2021) [35]	Economic Analysis and Policy	Government’s awareness of environmental protection			information disclosure
Xiang et al. (2022) [36]	International Review of Economics and Finance	Internal and external financing			debt financing and equity financing.
Jiang and Bai (2022) [37]	Technology in Society	institutional investors’ site visits		Firm size	CEO duality or low-competitive market environment.
Tan et al. (2022) [38]	Resources Policy	green credit policy		financing constraint or the fiercer the industrial competition and degree of marketization
Research on the outcomes of ESG
Author	Journal	Outcomes of ESG		Moderating Variables	Mediating Variables
Gillan et al. (2021) [39]	Journal of Corporate Finance	Corporate Social Responsibility		Institutional investor ownership
Bofinger et al. (2022) [40]	Journal of Banking and Finance	Market efficiency		market sentiment toward sustainability
Nekhili et al. (2021) [41]	Corporate Governance: An International Review	financial performance		employee board representation
Chen and Xie (2022) [42]	International Review of Financial Analysis	financial performance		ESG investors
Tang (2022) [43]	Sustainability	corporate Innovation			Financial Constraints and Agency Cost
Zhou, Liu, and Luo (2022) [44]	Business Strategy and the Environment	company market value			financial performance

Table 2

The descriptions of the variables.

	Variables	Description
Dependent Variable	Green innovation.	We applied the natural logarithm of the sum of the total number of applications of green innovation patents and utility patents plus 1 to measure corporate green innovation (GRInno). We classified the application of green innovation patents as exploratory green innovation (GRInva) and the application of utility patents as exploitative green innovation (GRUma).
Independent Variable	ESG rating	All listed companies were rated by the China Securities ESG rating score
Moderation Variables	Institutional Environment	the degree of marketization is often used as its proxy variable
	Redundant Organizational Resources	The cash ratio was adopted to determine the redundant organizational resources given their importance in firms′ green innovation, in reference to the research of Kim and Bettis (2014). Specifically, cash ratio = (Cash + marketable securities)/current liabilities)
Control Variables	Size	The natural logarithm of the total assets
	Age	The number of years that the firm has been established.
	Top1	Percentage of shares held by the largest shareholder
	ROA	Net profit divided by average total assets
	Lev	The total liabilities divided by the total assets
	Board	The natural logarithm of the number of directors on the board
	Inde	The number of independent directors divided by the total number of all directors.
	Dualiy	Whether the chairman and the general manager are the same people

Table 3

Descriptive statistics and correlation analysis.

Variables	Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. GRInno	0.445	0.852	1
2. GRInva	0.301	0.678	0.928 ***	1
3. GRUma	0.269	0.619	0.881 ***	0.694 ***	1
4. ESG	4.070	1.054	0.159 ***	0.155 ***	0.131 ***	1
5. InEnv	8.150	1.934	0.105 ***	0.101 ***	0.085 ***	0.070 ***	1
6. OrRes	0.968	1.698	−0.066 ***	−0.055 ***	−0.075 ***	0.074 ***	−0.013 **	1
7. Size	22.025	1.246	0.237 ***	0.246 ***	0.218 ***	0.151 ***	0.001	−0.287 ***	1
8. Age	2.792	0.355	−0.016 **	0.005	−0.026 ***	−0.065 ***	0.113 ***	−0.196 ***	0.175 ***	1
9. Top1	0.369	0.149	0.018 ***	0.007	0.036 ***	0.104 ***	0.021 ***	0.066 ***	0.119 ***	−0.170 ***	1
10. Roa	0.039	0.061	0.041 ***	0.043 ***	0.025 ***	0.230 ***	0.054 ***	0.205 ***	−0.011 *	−0.107 ***	0.175 ***	1
11. Lev	0.415	0.206	0.093 ***	0.088 ***	0.105 ***	−0.110 ***	−0.115 ***	−0.544 ***	0.489 ***	0.176 ***	−0.043 ***	−0.377 ***	1
12. Board	2.251	0.177	0.049 ***	0.053 ***	0.045 ***	0.029 ***	−0.142 ***	−0.075 ***	0.269 ***	0.020 ***	−0.013 **	0.013 **	0.166 ***	1
13. Inde	0.374	0.053	0.009	0.010	0.010 *	0.071 ***	0.043 ***	0.012 **	0.003	−0.017 ***	0.054 ***	−0.019 ***	−0.017 ***	−0.516 ***	1
14. Dualiy	0.274	0.446	0.006	0.003	0.001	−0.009	0.147 ***	0.103 ***	−0.187 ***	−0.083 ***	−0.00700	0.045 ***	−0.158 ***	−0.185 ***	0.114 ***	1

Notes: N = 21,616. *** correlation is significant at 1%. ** correlation is significant at 5%. * correlation is significant at 10%. t-statistics in parentheses.

Table 4

The regression results of ESG ratings on corporate green innovation.

	(1)	(2)	(3)
	GRInno	GRInva	GRUma
ESG	0.019 ***	0.017 ***	0.011 ***
	(3.789)	(4.247)	(2.968)
Size	0.046 ***	0.043 ***	0.026 ***
	(4.405)	(4.968)	(3.158)
Age	0.118 *	0.116 **	0.055
	(1.806)	(2.153)	(1.071)
Top1	−0.075	−0.012	−0.070
	(−1.199)	(−0.237)	(−1.438)
Roa	0.519 ***	0.306 ***	0.380 ***
	(5.679)	(4.063)	(5.338)
Lev	0.121 ***	0.101 ***	0.068 **
	(2.938)	(2.988)	(2.139)
Board	0.015	0.016	−0.018
	(0.327)	(0.406)	(−0.490)
Inde	−0.069	−0.037	−0.070
	(−0.540)	(−0.350)	(−0.701)
Dualiy	0.002	−0.005	0.002
	(0.160)	(−0.430)	(0.176)
Constant	−1.000 ***	−1.081 ***	−0.433 *
	(−3.247)	(−4.262)	(−1.804)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	21,616	21,616	21,616
Adj.R2	0.692	0.673	0.643

Notes: *** correlation is significant at 1%. ** correlation is significant at 5%. * correlation is significant at 10%. t-statistics in parentheses.

Table 5

The moderating effects of the institutional environment and redundant organizational resources.

	(1)	(2)	(3)
	GRInno	GRInno	GRInno
ESG	0.019 ***	0.018 ***	0.019 ***
	(3.939)	(3.647)	(3.799)
InEnv	−0.004		−0.004
	(−0.360)		(−0.390)
ESG × InEnv	0.009 ***		0.009 ***
	(3.819)		(3.790)
OrRes		0.001	0.001
		(0.449)	(0.461)
ESG × OrRes		0.067 **	0.066 **
		(2.140)	(2.092)
Size	0.046***	0.045 ***	0.046 ***
	(4.447)	(4.337)	(4.380)
Age	0.113 *	0.116 *	0.112 *
	(1.732)	(1.765)	(1.695)
Top1	−0.079	−0.075	−0.079
	(−1.265)	(−1.198)	(−1.264)
Roa	0.520 ***	0.518 ***	0.519 ***
	(5.690)	(5.667)	(5.679)
Lev	0.118 ***	0.129 ***	0.126 ***
	(2.881)	(2.923)	(2.875)
Board	0.014	0.017	0.015
	(0.289)	(0.365)	(0.327)
Inde	−0.058	−0.069	−0.058
	(−0.452)	(−0.537)	(−0.450)
Dualiy	0.002	0.002	0.002
	(0.122)	(0.157)	(0.119)
Constant	−1.261 ***	−1.265 ***	−1.248 ***
	(−3.004)	(−2.993)	(−2.958)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	21,616	21,616	21,616
Adj.R2	0.692	0.692	0.692

Notes: *** correlation is significant at 1%. ** correlation is significant at 5%. * correlation is significant at 10%. t-statistics in parentheses.

Table 6

Substitute variables for corporate green innovation.

	(1)	(2)	(3)	(4)
	GRInva	GRUma	GRInva	GRUma
ESG	0.018 ***	0.012 ***	0.017 ***	0.011 ***
	(4.421)	(3.097)	(4.118)	(2.796)
InEnv	0.002	−0.002
	(0.189)	(−0.191)
ESG × InEnv	0.009 ***	0.006 ***
	(4.315)	(3.248)
OrRes			0.004	0.002
			(1.491)	(0.666)
ESG × OrRes			0.065 ***	0.064 ***
			(2.653)	(2.605)
Size	0.043 ***	0.026 ***	0.042 ***	0.025 ***
	(5.009)	(3.192)	(4.887)	(3.073)
Age	0.111 **	0.051	0.123 **	0.054
	(2.068)	(1.007)	(2.272)	(1.045)
Top1	−0.016	−0.073	−0.015	−0.070
	(−0.305)	(−1.493)	(−0.286)	(−1.442)
Roa	0.306 ***	0.381 ***	0.308 ***	0.380 ***
	(4.071)	(5.346)	(4.096)	(5.328)
Lev	0.098 ***	0.067 **	0.121 ***	0.078 **
	(2.907)	(2.087)	(3.336)	(2.260)
Board	0.014	−0.019	0.017	−0.016
	(0.367)	(−0.522)	(0.436)	(−0.442)
Inde	−0.026	−0.063	−0.034	−0.070
	(−0.244)	(−0.625)	(−0.319)	(−0.695)
Dualiy	−0.005	0.001	−0.005	0.002
	(−0.478)	(0.143)	(−0.480)	(0.168)
Constant	−1.004 ***	−0.383	−1.027 ***	−0.376
	(−3.953)	(−1.593)	(−4.029)	(−1.559)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	21616	21616	21616	21616
Adj.R2	0.674	0.644	0.673	0.644

Notes: *** correlation is significant at 1%. ** correlation is significant at 5%. t-statistics in parentheses.

Table 7

The change of the measurement method of standard errors.

	(1)	(2)	(3)
	GRInno	GRInno	GRInno
ESG	0.019 ***	0.018 ***	0.019 ***
	(3.279)	(3.078)	(3.173)
InEnv	−0.004		−0.004
	(−0.254)		(−0.275)
ESG × InEnv	0.009 ***		0.009 ***
	(3.341)		(3.321)
OrRes		(0.421)	(0.433)
		0.067 *	0.066 *
ESG × OrRes		(1.909)	(1.879)
		(0.421)	(0.433)
Size	0.046 ***	0.045 ***	0.046 ***
	(2.928)	(2.862)	(2.893)
Age	0.113	0.116	0.112
	(1.062)	(1.078)	(1.037)
Top1	−0.079	−0.075	−0.079
	(−0.877)	(−0.830)	(−0.877)
Roa	0.520 ***	0.518 ***	0.519 ***
	(5.053)	(5.031)	(5.040)
Lev	0.118 **	0.129 **	0.126 **
	(2.325)	(2.374)	(2.339)
Board	0.014	0.017	0.015
	(0.223)	(0.282)	(0.253)
Inde	−0.058	−0.069	−0.058
	(−0.347)	(−0.412)	(−0.345)
Dualiy	0.002	0.002	0.002
	(0.102)	(0.130)	(0.099)
Constant	−1.007 **	−1.000 **	−0.998 **
	(−2.110)	(−2.078)	(−2.079)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	21616	21616	21616
Adj.R2	0.043	0.042	0.043

Notes: *** correlation is significant at 1%. ** correlation is significant at 5%. * correlation is significant at 10%. t-statistics in parentheses.

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